This book focuses on the comparison, contrast, and assessment of risks on the basis of clinical investigations. It develops basic concepts as well as deriving biostatistical methods through both the application of classical mathematical statistical tools and more modern likelihood-based theories. The first half of the book presents methods for the analysis of single and multiple 2x2 tables for cross-sectional, prospective, and retrospective (case-control) sampling, with and without matching using fixed and two-stage random effects models.
The text then moves on to present a more modern likelihood- or model-based approach, which includes unconditional and conditional logistic regression; the analysis of count data and the Poisson regression model; the analysis of event time data, including the proportional hazards and multiplicative intensity models; and elements of categorical data analysis (expanded in this edition). SAS subroutines are both showcased in the text and embellished online by way of a dedicated author website. The book contains a technical, but accessible appendix that presents the core mathematical statistical theory used for the development of classical and modern statistical methods.
The author of this book has made a tremendous effort in covering a gamut of tests, methods, and ideas for biostatistical problem solving . . . In conclusion, the book is recommended to all in biostatistics as a technical reference. (Journal of Biopharmaceutical Statistics, 1 September 2012)
..".Biostatistics is set apart from other statistics specialties by its focus on the assessment of risks and relative risks through clinical research," states Lachin (George Washington U.) in the preface to the first edition (2001). He developed this graduate text to support a course he launched as a joint initiative of the university's department of statistics, its Biostatistics Center, and the School of Public Health and Health Services. Coverage includes discussion of biostatistics and biomedical science, relative risk estimates and tests for independent groups, sample size, stratified adjusted analysis, case-control and matched studies, applications of maximum likelihood and efficient scores
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